Overview

Dataset statistics

Number of variables23
Number of observations1427854
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory197.0 MiB
Average record size in memory144.7 B

Variable types

Numeric13
Categorical9
DateTime1

Alerts

BoolBridle has constant value "0"Constant
Town has a high cardinality: 1960 distinct valuesHigh cardinality
No_Incidents is highly overall correlated with Risk_S*I/Inspections and 3 other fieldsHigh correlation
Risk_S*I/Inspections is highly overall correlated with No_Incidents and 4 other fieldsHigh correlation
leakage_estimate_factor is highly overall correlated with No_Incidents and 2 other fieldsHigh correlation
Risk_S*I is highly overall correlated with No_Incidents and 3 other fieldsHigh correlation
Length is highly overall correlated with NumConnectionsHigh correlation
NumConnections is highly overall correlated with LengthHigh correlation
Severity is highly overall correlated with Risk_S*I/Inspections and 1 other fieldsHigh correlation
Incidence is highly overall correlated with No_Incidents and 3 other fieldsHigh correlation
Severity is highly imbalanced (98.8%)Imbalance
Incidence is highly imbalanced (97.9%)Imbalance
Material is highly imbalanced (77.7%)Imbalance
NumConnectionsUnder is highly imbalanced (99.8%)Imbalance
gas_natural is highly imbalanced (76.0%)Imbalance
leakage_estimate_factor is highly skewed (γ1 = 31.24044418)Skewed
Length is highly skewed (γ1 = 61.11727142)Skewed
PipeId has unique valuesUnique
No_Incidents has 1416383 (99.2%) zerosZeros
Risk_S*I/Inspections has 1416383 (99.2%) zerosZeros
leakage_estimate_factor has 1416432 (99.2%) zerosZeros
Risk_S*I has 1416383 (99.2%) zerosZeros
NumConnections has 885989 (62.1%) zerosZeros

Reproduction

Analysis started2023-02-12 23:35:27.814406
Analysis finished2023-02-12 23:37:20.027077
Duration1 minute and 52.21 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

PipeId
Real number (ℝ)

Distinct1427854
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8729451 × 108
Minimum489616
Maximum4.5199531 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.3 MiB
2023-02-13T00:37:20.139883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum489616
5-th percentile9762584.2
Q152652587
median1.8972642 × 108
Q32.9082495 × 108
95-th percentile3.9839603 × 108
Maximum4.5199531 × 108
Range4.5150569 × 108
Interquartile range (IQR)2.3817236 × 108

Descriptive statistics

Standard deviation1.2080573 × 108
Coefficient of variation (CV)0.64500412
Kurtosis-0.95896068
Mean1.8729451 × 108
Median Absolute Deviation (MAD)1.0947898 × 108
Skewness0.084257286
Sum2.6742922 × 1014
Variance1.4594025 × 1016
MonotonicityNot monotonic
2023-02-13T00:37:20.260128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56922465 1
 
< 0.1%
188184697 1
 
< 0.1%
308827198 1
 
< 0.1%
188180179 1
 
< 0.1%
51286512 1
 
< 0.1%
7941903 1
 
< 0.1%
188184880 1
 
< 0.1%
190623079 1
 
< 0.1%
31530581 1
 
< 0.1%
132954227 1
 
< 0.1%
Other values (1427844) 1427844
> 99.9%
ValueCountFrequency (%)
489616 1
< 0.1%
489645 1
< 0.1%
489646 1
< 0.1%
489780 1
< 0.1%
489790 1
< 0.1%
489792 1
< 0.1%
489793 1
< 0.1%
489981 1
< 0.1%
489982 1
< 0.1%
489996 1
< 0.1%
ValueCountFrequency (%)
451995309 1
< 0.1%
451995260 1
< 0.1%
451995254 1
< 0.1%
451195580 1
< 0.1%
451195430 1
< 0.1%
451195406 1
< 0.1%
451195391 1
< 0.1%
451195364 1
< 0.1%
451195284 1
< 0.1%
451195194 1
< 0.1%

Inspections
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4439726
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.3 MiB
2023-02-13T00:37:20.352800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q35
95-th percentile6
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3786941
Coefficient of variation (CV)0.31023912
Kurtosis0.80788533
Mean4.4439726
Median Absolute Deviation (MAD)0
Skewness-0.89492045
Sum6345344
Variance1.9007975
MonotonicityNot monotonic
2023-02-13T00:37:20.428996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5 781700
54.7%
6 196586
 
13.8%
2 134877
 
9.4%
4 124383
 
8.7%
3 116200
 
8.1%
1 64846
 
4.5%
7 4642
 
0.3%
10 2216
 
0.2%
8 938
 
0.1%
9 844
 
0.1%
ValueCountFrequency (%)
1 64846
 
4.5%
2 134877
 
9.4%
3 116200
 
8.1%
4 124383
 
8.7%
5 781700
54.7%
6 196586
 
13.8%
7 4642
 
0.3%
8 938
 
0.1%
9 844
 
0.1%
10 2216
 
0.2%
ValueCountFrequency (%)
11 622
 
< 0.1%
10 2216
 
0.2%
9 844
 
0.1%
8 938
 
0.1%
7 4642
 
0.3%
6 196586
 
13.8%
5 781700
54.7%
4 124383
 
8.7%
3 116200
 
8.1%
2 134877
 
9.4%

No_Incidents
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.008633936
Minimum0
Maximum5
Zeros1416383
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size16.3 MiB
2023-02-13T00:37:20.501339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.099314918
Coefficient of variation (CV)11.502856
Kurtosis197.85137
Mean0.008633936
Median Absolute Deviation (MAD)0
Skewness12.897846
Sum12328
Variance0.0098634529
MonotonicityNot monotonic
2023-02-13T00:37:20.581244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1416383
99.2%
1 10681
 
0.7%
2 729
 
0.1%
3 56
 
< 0.1%
4 4
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 1416383
99.2%
1 10681
 
0.7%
2 729
 
0.1%
3 56
 
< 0.1%
4 4
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 4
 
< 0.1%
3 56
 
< 0.1%
2 729
 
0.1%
1 10681
 
0.7%
0 1416383
99.2%

Risk_S*I/Inspections
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0089574506
Minimum0
Maximum3
Zeros1416383
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size16.3 MiB
2023-02-13T00:37:20.688944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.11525577
Coefficient of variation (CV)12.867029
Kurtosis338.33746
Mean0.0089574506
Median Absolute Deviation (MAD)0
Skewness16.808451
Sum12789.932
Variance0.013283893
MonotonicityNot monotonic
2023-02-13T00:37:20.806082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1416383
99.2%
0.7599999905 3019
 
0.2%
0.6388888955 1603
 
0.1%
1.75 1562
 
0.1%
0.7200000286 953
 
0.1%
1.222222209 878
 
0.1%
3 757
 
0.1%
0.6800000072 471
 
< 0.1%
0.9375 406
 
< 0.1%
0.6111111045 277
 
< 0.1%
Other values (46) 1545
 
0.1%
ValueCountFrequency (%)
0 1416383
99.2%
0.3700000048 4
 
< 0.1%
0.3799999952 2
 
< 0.1%
0.3899999857 17
 
< 0.1%
0.407407403 1
 
< 0.1%
0.4197530746 1
 
< 0.1%
0.4320987761 3
 
< 0.1%
0.453125 1
 
< 0.1%
0.46875 2
 
< 0.1%
0.484375 11
 
< 0.1%
ValueCountFrequency (%)
3 757
0.1%
2.666666746 2
 
< 0.1%
2.5 12
 
< 0.1%
2.4375 6
 
< 0.1%
2.222222328 64
 
< 0.1%
2.039999962 13
 
< 0.1%
2 146
 
< 0.1%
1.919999957 5
 
< 0.1%
1.799999952 2
 
< 0.1%
1.777777791 4
 
< 0.1%

leakage_estimate_factor
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct393
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10518317
Minimum0
Maximum198
Zeros1416432
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size16.3 MiB
2023-02-13T00:37:20.932145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum198
Range198
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5055693
Coefficient of variation (CV)14.313786
Kurtosis1935.8081
Mean0.10518317
Median Absolute Deviation (MAD)0
Skewness31.240444
Sum150186.21
Variance2.2667391
MonotonicityNot monotonic
2023-02-13T00:37:21.055282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1416432
99.2%
9.119999886 1820
 
0.1%
7.027777672 722
 
0.1%
8.640000343 699
 
< 0.1%
21 648
 
< 0.1%
8.739999771 397
 
< 0.1%
8.359999657 367
 
< 0.1%
9.5 343
 
< 0.1%
7.347222328 314
 
< 0.1%
7.666666508 297
 
< 0.1%
Other values (383) 5815
 
0.4%
ValueCountFrequency (%)
0 1416432
99.2%
0.5 1
 
< 0.1%
0.5699999928 1
 
< 0.1%
0.5849999785 1
 
< 0.1%
0.9750000238 1
 
< 0.1%
1 7
 
< 0.1%
1.019999981 1
 
< 0.1%
1.2109375 1
 
< 0.1%
1.458333373 1
 
< 0.1%
1.5 11
 
< 0.1%
ValueCountFrequency (%)
198 1
 
< 0.1%
180 1
 
< 0.1%
165 2
< 0.1%
163.5 2
< 0.1%
162 1
 
< 0.1%
144 3
< 0.1%
141 1
 
< 0.1%
133.5 1
 
< 0.1%
127.5 1
 
< 0.1%
125 1
 
< 0.1%

InspectionDay
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.8 MiB
Tuesday
292773 
Wednesday
286392 
Monday
285921 
Thursday
281702 
Friday
218370 
Other values (2)
62696 

Length

Max length9
Median length8
Mean length7.2592821
Min length6

Characters and Unicode

Total characters10365195
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThursday
2nd rowThursday
3rd rowThursday
4th rowThursday
5th rowThursday

Common Values

ValueCountFrequency (%)
Tuesday 292773
20.5%
Wednesday 286392
20.1%
Monday 285921
20.0%
Thursday 281702
19.7%
Friday 218370
15.3%
Saturday 41359
 
2.9%
Sunday 21337
 
1.5%

Length

2023-02-13T00:37:21.171236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T00:37:21.293214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
tuesday 292773
20.5%
wednesday 286392
20.1%
monday 285921
20.0%
thursday 281702
19.7%
friday 218370
15.3%
saturday 41359
 
2.9%
sunday 21337
 
1.5%

Most occurring characters

ValueCountFrequency (%)
d 1714246
16.5%
a 1469213
14.2%
y 1427854
13.8%
e 865557
8.4%
s 860867
8.3%
u 637171
 
6.1%
n 593650
 
5.7%
T 574475
 
5.5%
r 541431
 
5.2%
W 286392
 
2.8%
Other values (7) 1394339
13.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8937341
86.2%
Uppercase Letter 1427854
 
13.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 1714246
19.2%
a 1469213
16.4%
y 1427854
16.0%
e 865557
9.7%
s 860867
9.6%
u 637171
 
7.1%
n 593650
 
6.6%
r 541431
 
6.1%
o 285921
 
3.2%
h 281702
 
3.2%
Other values (2) 259729
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
T 574475
40.2%
W 286392
20.1%
M 285921
20.0%
F 218370
 
15.3%
S 62696
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 10365195
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 1714246
16.5%
a 1469213
14.2%
y 1427854
13.8%
e 865557
8.4%
s 860867
8.3%
u 637171
 
6.1%
n 593650
 
5.7%
T 574475
 
5.5%
r 541431
 
5.2%
W 286392
 
2.8%
Other values (7) 1394339
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10365195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 1714246
16.5%
a 1469213
14.2%
y 1427854
13.8%
e 865557
8.4%
s 860867
8.3%
u 637171
 
6.1%
n 593650
 
5.7%
T 574475
 
5.5%
r 541431
 
5.2%
W 286392
 
2.8%
Other values (7) 1394339
13.5%

InspectionYear
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.2919
Minimum2010
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.3 MiB
2023-02-13T00:37:21.396461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2018
Q12019
median2019
Q32020
95-th percentile2020
Maximum2021
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0062237
Coefficient of variation (CV)0.00049830519
Kurtosis17.58833
Mean2019.2919
Median Absolute Deviation (MAD)1
Skewness-3.4349364
Sum2.8832541 × 109
Variance1.012486
MonotonicityNot monotonic
2023-02-13T00:37:21.480589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2019 674030
47.2%
2020 656873
46.0%
2018 32287
 
2.3%
2017 30592
 
2.1%
2016 9895
 
0.7%
2015 7461
 
0.5%
2013 6177
 
0.4%
2014 5926
 
0.4%
2012 2166
 
0.2%
2021 1445
 
0.1%
Other values (2) 1002
 
0.1%
ValueCountFrequency (%)
2010 50
 
< 0.1%
2011 952
 
0.1%
2012 2166
 
0.2%
2013 6177
 
0.4%
2014 5926
 
0.4%
2015 7461
 
0.5%
2016 9895
 
0.7%
2017 30592
 
2.1%
2018 32287
 
2.3%
2019 674030
47.2%
ValueCountFrequency (%)
2021 1445
 
0.1%
2020 656873
46.0%
2019 674030
47.2%
2018 32287
 
2.3%
2017 30592
 
2.1%
2016 9895
 
0.7%
2015 7461
 
0.5%
2014 5926
 
0.4%
2013 6177
 
0.4%
2012 2166
 
0.2%
Distinct3041
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size21.8 MiB
Minimum2010-10-01 00:00:00
Maximum2020-12-31 00:00:00
2023-02-13T00:37:21.594767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:21.714127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

MonthsLastRev
Real number (ℝ)

Distinct128
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.74752
Minimum0
Maximum132
Zeros727
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size16.3 MiB
2023-02-13T00:37:21.839058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22
Q124
median24
Q324
95-th percentile26
Maximum132
Range132
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.252464
Coefficient of variation (CV)0.25265013
Kurtosis40.644295
Mean24.74752
Median Absolute Deviation (MAD)0
Skewness5.1776128
Sum35335845
Variance39.093306
MonotonicityNot monotonic
2023-02-13T00:37:21.960914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 884791
62.0%
25 154980
 
10.9%
23 152541
 
10.7%
22 87421
 
6.1%
48 29299
 
2.1%
21 26477
 
1.9%
26 18466
 
1.3%
49 6819
 
0.5%
47 4902
 
0.3%
20 4253
 
0.3%
Other values (118) 57905
 
4.1%
ValueCountFrequency (%)
0 727
0.1%
1 113
 
< 0.1%
2 370
 
< 0.1%
3 769
0.1%
4 716
0.1%
5 696
< 0.1%
6 665
< 0.1%
7 919
0.1%
8 961
0.1%
9 1174
0.1%
ValueCountFrequency (%)
132 1
 
< 0.1%
131 1
 
< 0.1%
130 2
 
< 0.1%
128 2
 
< 0.1%
125 3
 
< 0.1%
122 4
 
< 0.1%
121 17
< 0.1%
120 33
< 0.1%
119 12
 
< 0.1%
118 3
 
< 0.1%

Risk_S*I
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.030705905
Minimum0
Maximum15
Zeros1416383
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size16.3 MiB
2023-02-13T00:37:22.081129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.35262242
Coefficient of variation (CV)11.483864
Kurtosis180.5955
Mean0.030705905
Median Absolute Deviation (MAD)0
Skewness12.585851
Sum43843.549
Variance0.12434257
MonotonicityNot monotonic
2023-02-13T00:37:22.195230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1416383
99.2%
3.799999952 3020
 
0.2%
3.5 1811
 
0.1%
3.833333254 1603
 
0.1%
3.666666746 997
 
0.1%
3.599999905 952
 
0.1%
3 848
 
0.1%
3.400000095 471
 
< 0.1%
3.75 408
 
< 0.1%
7.333333492 159
 
< 0.1%
Other values (47) 1202
 
0.1%
ValueCountFrequency (%)
0 1416383
99.2%
1 81
 
< 0.1%
2 140
 
< 0.1%
2.5 50
 
< 0.1%
3 848
 
0.1%
3.25 45
 
< 0.1%
3.333333254 108
 
< 0.1%
3.400000095 471
 
< 0.1%
3.5 1811
 
0.1%
3.571428537 9
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
13.33333302 2
 
< 0.1%
12 1
 
< 0.1%
10.71428585 1
 
< 0.1%
10.66666698 1
 
< 0.1%
10.5 11
< 0.1%
10.19999981 13
< 0.1%
10 3
 
< 0.1%
9.75 6
< 0.1%
9.600000381 5
 
< 0.1%

Severity
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.8 MiB
4
1424923 
3
 
2179
2
 
545
1
 
207

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1427854
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 1424923
99.8%
3 2179
 
0.2%
2 545
 
< 0.1%
1 207
 
< 0.1%

Length

2023-02-13T00:37:22.304378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T00:37:22.403180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
4 1424923
99.8%
3 2179
 
0.2%
2 545
 
< 0.1%
1 207
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
4 1424923
99.8%
3 2179
 
0.2%
2 545
 
< 0.1%
1 207
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1427854
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1424923
99.8%
3 2179
 
0.2%
2 545
 
< 0.1%
1 207
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1427854
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 1424923
99.8%
3 2179
 
0.2%
2 545
 
< 0.1%
1 207
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1427854
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 1424923
99.8%
3 2179
 
0.2%
2 545
 
< 0.1%
1 207
 
< 0.1%

Incidence
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.8 MiB
0
1424923 
1
 
2931

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1427854
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1424923
99.8%
1 2931
 
0.2%

Length

2023-02-13T00:37:22.485228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T00:37:22.578256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1424923
99.8%
1 2931
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1424923
99.8%
1 2931
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1427854
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1424923
99.8%
1 2931
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1427854
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1424923
99.8%
1 2931
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1427854
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1424923
99.8%
1 2931
 
0.2%

Province
Categorical

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.8 MiB
Barcelona
383967 
Valencia
136075 
Madrid
119126 
Girona
85030 
Tarragona
75613 
Other values (33)
628043 

Length

Max length11
Median length10
Mean length7.8627409
Min length4

Characters and Unicode

Total characters11226846
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowValencia
2nd rowBarcelona
3rd rowValencia
4th rowValencia
5th rowBarcelona

Common Values

ValueCountFrequency (%)
Barcelona 383967
26.9%
Valencia 136075
 
9.5%
Madrid 119126
 
8.3%
Girona 85030
 
6.0%
Tarragona 75613
 
5.3%
Alicante 63681
 
4.5%
La Coruña 44670
 
3.1%
Sevilla 43903
 
3.1%
Toledo 39207
 
2.7%
Pontevedra 38736
 
2.7%
Other values (28) 397846
27.9%

Length

2023-02-13T00:37:22.668169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
barcelona 383967
25.3%
valencia 136075
 
9.0%
madrid 119126
 
7.8%
girona 85030
 
5.6%
tarragona 75613
 
5.0%
la 66409
 
4.4%
alicante 63681
 
4.2%
coruña 44670
 
2.9%
sevilla 43903
 
2.9%
toledo 39207
 
2.6%
Other values (30) 461228
30.4%

Most occurring characters

ValueCountFrequency (%)
a 2322089
20.7%
l 1053570
9.4%
e 945016
8.4%
r 921676
 
8.2%
n 904191
 
8.1%
o 837574
 
7.5%
c 631892
 
5.6%
i 601135
 
5.4%
d 535799
 
4.8%
B 401733
 
3.6%
Other values (28) 2072171
18.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9616882
85.7%
Uppercase Letter 1518909
 
13.5%
Space Separator 91055
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2322089
24.1%
l 1053570
11.0%
e 945016
9.8%
r 921676
 
9.6%
n 904191
 
9.4%
o 837574
 
8.7%
c 631892
 
6.6%
i 601135
 
6.3%
d 535799
 
5.6%
t 149494
 
1.6%
Other values (12) 714446
 
7.4%
Uppercase Letter
ValueCountFrequency (%)
B 401733
26.4%
V 173339
11.4%
M 150955
 
9.9%
L 138589
 
9.1%
C 135862
 
8.9%
T 115659
 
7.6%
G 115363
 
7.6%
A 80422
 
5.3%
S 72373
 
4.8%
P 47901
 
3.2%
Other values (5) 86713
 
5.7%
Space Separator
ValueCountFrequency (%)
91055
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11135791
99.2%
Common 91055
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2322089
20.9%
l 1053570
9.5%
e 945016
8.5%
r 921676
 
8.3%
n 904191
 
8.1%
o 837574
 
7.5%
c 631892
 
5.7%
i 601135
 
5.4%
d 535799
 
4.8%
B 401733
 
3.6%
Other values (27) 1981116
17.8%
Common
ValueCountFrequency (%)
91055
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11060112
98.5%
None 166734
 
1.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2322089
21.0%
l 1053570
9.5%
e 945016
8.5%
r 921676
 
8.3%
n 904191
 
8.2%
o 837574
 
7.6%
c 631892
 
5.7%
i 601135
 
5.4%
d 535799
 
4.8%
B 401733
 
3.6%
Other values (24) 1905437
17.2%
None
ValueCountFrequency (%)
ó 76599
45.9%
ñ 44670
26.8%
á 39472
23.7%
é 5993
 
3.6%

Town
Categorical

Distinct1960
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.8 MiB
Madrid
 
75616
Barcelona
 
58408
Valencia
 
25405
Sevilla
 
22491
Terrassa
 
16573
Other values (1955)
1229361 

Length

Max length25
Median length22
Mean length10.504441
Min length3

Characters and Unicode

Total characters14998808
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)< 0.1%

Sample

1st rowBetera
2nd rowSabadell
3rd rowBetera
4th rowBetera
5th rowSabadell

Common Values

ValueCountFrequency (%)
Madrid 75616
 
5.3%
Barcelona 58408
 
4.1%
Valencia 25405
 
1.8%
Sevilla 22491
 
1.6%
Terrassa 16573
 
1.2%
Málaga 16363
 
1.1%
Sabadell 15887
 
1.1%
Vigo 14213
 
1.0%
Alicante/Alacant 13899
 
1.0%
Valladolid 13369
 
0.9%
Other values (1950) 1155630
80.9%

Length

2023-02-13T00:37:22.782364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 223555
 
9.8%
madrid 75616
 
3.3%
del 75467
 
3.3%
sant 61713
 
2.7%
la 58728
 
2.6%
barcelona 58408
 
2.6%
valles 31698
 
1.4%
llobregat 26697
 
1.2%
valencia 26038
 
1.1%
sevilla 22491
 
1.0%
Other values (2139) 1627069
71.1%

Most occurring characters

ValueCountFrequency (%)
a 2289631
15.3%
e 1431172
 
9.5%
l 1372557
 
9.2%
r 1012677
 
6.8%
859641
 
5.7%
o 844774
 
5.6%
n 756321
 
5.0%
d 731066
 
4.9%
i 697670
 
4.7%
s 558622
 
3.7%
Other values (55) 4444677
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11993684
80.0%
Uppercase Letter 2043798
 
13.6%
Space Separator 859641
 
5.7%
Other Punctuation 79065
 
0.5%
Dash Punctuation 22612
 
0.2%
Decimal Number 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2289631
19.1%
e 1431172
11.9%
l 1372557
11.4%
r 1012677
8.4%
o 844774
 
7.0%
n 756321
 
6.3%
d 731066
 
6.1%
i 697670
 
5.8%
s 558622
 
4.7%
t 501573
 
4.2%
Other values (23) 1797621
15.0%
Uppercase Letter
ValueCountFrequency (%)
M 245922
12.0%
S 227354
11.1%
C 227219
11.1%
V 201057
9.8%
A 178438
8.7%
B 163200
8.0%
P 122254
 
6.0%
L 110181
 
5.4%
R 93596
 
4.6%
T 89573
 
4.4%
Other values (16) 385004
18.8%
Other Punctuation
ValueCountFrequency (%)
/ 50954
64.4%
' 26016
32.9%
. 2095
 
2.6%
Space Separator
ValueCountFrequency (%)
859641
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22612
100.0%
Decimal Number
ValueCountFrequency (%)
7 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14037482
93.6%
Common 961326
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2289631
16.3%
e 1431172
 
10.2%
l 1372557
 
9.8%
r 1012677
 
7.2%
o 844774
 
6.0%
n 756321
 
5.4%
d 731066
 
5.2%
i 697670
 
5.0%
s 558622
 
4.0%
t 501573
 
3.6%
Other values (49) 3841419
27.4%
Common
ValueCountFrequency (%)
859641
89.4%
/ 50954
 
5.3%
' 26016
 
2.7%
- 22612
 
2.4%
. 2095
 
0.2%
7 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14851894
99.0%
None 146914
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2289631
15.4%
e 1431172
 
9.6%
l 1372557
 
9.2%
r 1012677
 
6.8%
859641
 
5.8%
o 844774
 
5.7%
n 756321
 
5.1%
d 731066
 
4.9%
i 697670
 
4.7%
s 558622
 
3.8%
Other values (44) 4297763
28.9%
None
ValueCountFrequency (%)
ñ 41082
28.0%
ó 30988
21.1%
á 30483
20.7%
í 11558
 
7.9%
é 11267
 
7.7%
ç 6777
 
4.6%
à 6129
 
4.2%
è 5464
 
3.7%
ú 2676
 
1.8%
Á 304
 
0.2%

YearBuilt
Real number (ℝ)

Distinct95
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002.3953
Minimum1901
Maximum2050
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.3 MiB
2023-02-13T00:37:22.898237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1901
5-th percentile1986
Q11998
median2004
Q32009
95-th percentile2016
Maximum2050
Range149
Interquartile range (IQR)11

Descriptive statistics

Standard deviation12.063572
Coefficient of variation (CV)0.0060245706
Kurtosis22.494039
Mean2002.3953
Median Absolute Deviation (MAD)5
Skewness-3.4324023
Sum2.8591282 × 109
Variance145.52977
MonotonicityNot monotonic
2023-02-13T00:37:23.018164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2002 86257
 
6.0%
2004 75048
 
5.3%
2003 68950
 
4.8%
2005 67861
 
4.8%
2001 67333
 
4.7%
2008 66563
 
4.7%
2006 64662
 
4.5%
2007 62125
 
4.4%
2016 56975
 
4.0%
2009 56945
 
4.0%
Other values (85) 755135
52.9%
ValueCountFrequency (%)
1901 6068
0.4%
1912 2
 
< 0.1%
1914 2
 
< 0.1%
1920 1
 
< 0.1%
1923 1
 
< 0.1%
1925 2
 
< 0.1%
1926 1
 
< 0.1%
1927 2
 
< 0.1%
1928 5
 
< 0.1%
1929 5
 
< 0.1%
ValueCountFrequency (%)
2050 29
 
< 0.1%
2022 21
 
< 0.1%
2021 230
 
< 0.1%
2020 2109
 
0.1%
2019 6192
 
0.4%
2018 14101
 
1.0%
2017 19957
 
1.4%
2016 56975
4.0%
2015 52253
3.7%
2014 36523
2.6%

Material
Categorical

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.8 MiB
PE
1232020 
AO
127213 
FD
 
44305
PN
 
15251
CU
 
6624
Other values (6)
 
2441

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2855708
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPE
2nd rowPE
3rd rowAO
4th rowAO
5th rowPE

Common Values

ValueCountFrequency (%)
PE 1232020
86.3%
AO 127213
 
8.9%
FD 44305
 
3.1%
PN 15251
 
1.1%
CU 6624
 
0.5%
ZD 2377
 
0.2%
FG 22
 
< 0.1%
PA 13
 
< 0.1%
FI 13
 
< 0.1%
PV 12
 
< 0.1%

Length

2023-02-13T00:37:23.127067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pe 1232020
86.3%
ao 127213
 
8.9%
fd 44305
 
3.1%
pn 15251
 
1.1%
cu 6624
 
0.5%
zd 2377
 
0.2%
fg 22
 
< 0.1%
pa 13
 
< 0.1%
fi 13
 
< 0.1%
pv 12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
P 1247296
43.7%
E 1232020
43.1%
A 127226
 
4.5%
O 127217
 
4.5%
D 46682
 
1.6%
F 44344
 
1.6%
N 15251
 
0.5%
C 6624
 
0.2%
U 6624
 
0.2%
Z 2377
 
0.1%
Other values (3) 47
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2855708
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 1247296
43.7%
E 1232020
43.1%
A 127226
 
4.5%
O 127217
 
4.5%
D 46682
 
1.6%
F 44344
 
1.6%
N 15251
 
0.5%
C 6624
 
0.2%
U 6624
 
0.2%
Z 2377
 
0.1%
Other values (3) 47
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2855708
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 1247296
43.7%
E 1232020
43.1%
A 127226
 
4.5%
O 127217
 
4.5%
D 46682
 
1.6%
F 44344
 
1.6%
N 15251
 
0.5%
C 6624
 
0.2%
U 6624
 
0.2%
Z 2377
 
0.1%
Other values (3) 47
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2855708
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 1247296
43.7%
E 1232020
43.1%
A 127226
 
4.5%
O 127217
 
4.5%
D 46682
 
1.6%
F 44344
 
1.6%
N 15251
 
0.5%
C 6624
 
0.2%
U 6624
 
0.2%
Z 2377
 
0.1%
Other values (3) 47
 
< 0.1%

Diameter
Real number (ℝ)

Distinct62
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.63386
Minimum10
Maximum609.59998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.3 MiB
2023-02-13T00:37:23.228112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile40
Q176.199997
median110
Q3160
95-th percentile203.2
Maximum609.59998
Range599.59998
Interquartile range (IQR)83.800003

Descriptive statistics

Standard deviation57.88525
Coefficient of variation (CV)0.49629883
Kurtosis4.4144392
Mean116.63386
Median Absolute Deviation (MAD)47
Skewness1.5130728
Sum1.6653613 × 108
Variance3350.7021
MonotonicityNot monotonic
2023-02-13T00:37:23.340901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 350517
24.5%
63 264611
18.5%
90 251628
17.6%
160 180501
12.6%
200 129395
 
9.1%
40 51319
 
3.6%
152.3999939 29432
 
2.1%
101.5999985 24268
 
1.7%
203.1999969 20836
 
1.5%
250 15951
 
1.1%
Other values (52) 109396
 
7.7%
ValueCountFrequency (%)
10 83
 
< 0.1%
11 31
 
< 0.1%
12 621
 
< 0.1%
12.69999981 7
 
< 0.1%
13 59
 
< 0.1%
14 29
 
< 0.1%
15 2078
0.1%
16 565
 
< 0.1%
18 9
 
< 0.1%
19 1768
0.1%
ValueCountFrequency (%)
609.5999756 147
 
< 0.1%
558.7999878 26
 
< 0.1%
508 1024
 
0.1%
500 45
 
< 0.1%
457.2000122 529
 
< 0.1%
406.3999939 3530
0.2%
400 120
 
< 0.1%
355.6000061 317
 
< 0.1%
355 28
 
< 0.1%
350 82
 
< 0.1%

Length
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct185608
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.792207
Minimum0
Maximum26100.943
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size16.3 MiB
2023-02-13T00:37:23.462914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.79900002
Q13.5
median13.598
Q344.377748
95-th percentile137.495
Maximum26100.943
Range26100.943
Interquartile range (IQR)40.877748

Descriptive statistics

Standard deviation78.154167
Coefficient of variation (CV)2.1242044
Kurtosis17570
Mean36.792207
Median Absolute Deviation (MAD)12.094
Skewness61.117271
Sum52533900
Variance6108.0737
MonotonicityNot monotonic
2023-02-13T00:37:23.582662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8817
 
0.6%
0.5 7786
 
0.5%
2 5995
 
0.4%
1.001999974 4854
 
0.3%
1.5 4172
 
0.3%
1.001000047 3024
 
0.2%
1.003000021 2869
 
0.2%
0.5009999871 2098
 
0.1%
1.200000048 1994
 
0.1%
3 1989
 
0.1%
Other values (185598) 1384256
96.9%
ValueCountFrequency (%)
0 4
 
< 0.1%
0.004999999888 9
 
< 0.1%
0.006000000052 10
 
< 0.1%
0.007000000216 9
 
< 0.1%
0.00800000038 16
 
< 0.1%
0.008999999613 16
 
< 0.1%
0.009999999776 43
< 0.1%
0.01099999994 21
< 0.1%
0.0120000001 13
 
< 0.1%
0.01300000027 21
< 0.1%
ValueCountFrequency (%)
26100.94336 1
< 0.1%
26030.14844 1
< 0.1%
7291.366211 1
< 0.1%
7281.373047 1
< 0.1%
5801.324219 1
< 0.1%
5128.307129 1
< 0.1%
4808.214844 1
< 0.1%
4738.890137 1
< 0.1%
4737.599121 1
< 0.1%
4690.916992 1
< 0.1%

Pressure
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0660291
Minimum0.025
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.3 MiB
2023-02-13T00:37:23.686405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.025
5-th percentile0.025
Q10.1
median0.15000001
Q34
95-th percentile16
Maximum80
Range79.975
Interquartile range (IQR)3.9

Descriptive statistics

Standard deviation6.9117017
Coefficient of variation (CV)2.2542844
Kurtosis41.851929
Mean3.0660291
Median Absolute Deviation (MAD)0.125
Skewness5.669332
Sum4377842
Variance47.771618
MonotonicityNot monotonic
2023-02-13T00:37:23.777467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4 410666
28.8%
0.150000006 362501
25.4%
0.02500000037 254694
17.8%
0.1000000015 126904
 
8.9%
16 81718
 
5.7%
1.700000048 63758
 
4.5%
0.400000006 56009
 
3.9%
5 39695
 
2.8%
49.5 10003
 
0.7%
0.05000000075 4927
 
0.3%
Other values (10) 16979
 
1.2%
ValueCountFrequency (%)
0.02500000037 254694
17.8%
0.05000000075 4927
 
0.3%
0.1000000015 126904
 
8.9%
0.150000006 362501
25.4%
0.400000006 56009
 
3.9%
1.700000048 63758
 
4.5%
2 4405
 
0.3%
4 410666
28.8%
5 39695
 
2.8%
10 1922
 
0.1%
ValueCountFrequency (%)
80 1180
 
0.1%
72 1408
 
0.1%
59.5 1332
 
0.1%
49.5 10003
 
0.7%
45 2350
 
0.2%
40 234
 
< 0.1%
36 2462
 
0.2%
25 197
 
< 0.1%
16 81718
5.7%
12 1489
 
0.1%

NumConnections
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.96511478
Minimum0
Maximum88
Zeros885989
Zeros (%)62.1%
Negative0
Negative (%)0.0%
Memory size16.3 MiB
2023-02-13T00:37:23.899584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum88
Range88
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.1584117
Coefficient of variation (CV)2.23643
Kurtosis57.135918
Mean0.96511478
Median Absolute Deviation (MAD)0
Skewness5.526978
Sum1378043
Variance4.658741
MonotonicityNot monotonic
2023-02-13T00:37:24.226857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 885989
62.1%
1 271523
 
19.0%
2 111389
 
7.8%
3 53893
 
3.8%
4 32683
 
2.3%
5 20320
 
1.4%
6 13963
 
1.0%
7 9230
 
0.6%
8 7026
 
0.5%
9 4887
 
0.3%
Other values (54) 16951
 
1.2%
ValueCountFrequency (%)
0 885989
62.1%
1 271523
 
19.0%
2 111389
 
7.8%
3 53893
 
3.8%
4 32683
 
2.3%
5 20320
 
1.4%
6 13963
 
1.0%
7 9230
 
0.6%
8 7026
 
0.5%
9 4887
 
0.3%
ValueCountFrequency (%)
88 1
 
< 0.1%
83 1
 
< 0.1%
79 1
 
< 0.1%
65 1
 
< 0.1%
63 2
< 0.1%
60 3
< 0.1%
59 1
 
< 0.1%
58 4
< 0.1%
55 2
< 0.1%
54 3
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.8 MiB
0
1427403 
1
 
418
2
 
28
3
 
4
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1427854
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1427403
> 99.9%
1 418
 
< 0.1%
2 28
 
< 0.1%
3 4
 
< 0.1%
4 1
 
< 0.1%

Length

2023-02-13T00:37:24.326230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T00:37:24.427950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1427403
> 99.9%
1 418
 
< 0.1%
2 28
 
< 0.1%
3 4
 
< 0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1427403
> 99.9%
1 418
 
< 0.1%
2 28
 
< 0.1%
3 4
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1427854
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1427403
> 99.9%
1 418
 
< 0.1%
2 28
 
< 0.1%
3 4
 
< 0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1427854
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1427403
> 99.9%
1 418
 
< 0.1%
2 28
 
< 0.1%
3 4
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1427854
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1427403
> 99.9%
1 418
 
< 0.1%
2 28
 
< 0.1%
3 4
 
< 0.1%
4 1
 
< 0.1%

BoolBridle
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.8 MiB
0
1427854 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1427854
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1427854
100.0%

Length

2023-02-13T00:37:24.513525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T00:37:24.606440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1427854
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1427854
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1427854
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1427854
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1427854
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1427854
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1427854
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1427854
100.0%

gas_natural
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.8 MiB
1
1371420 
0
 
56434

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1427854
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1371420
96.0%
0 56434
 
4.0%

Length

2023-02-13T00:37:24.679023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T00:37:24.773289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1371420
96.0%
0 56434
 
4.0%

Most occurring characters

ValueCountFrequency (%)
1 1371420
96.0%
0 56434
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1427854
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1371420
96.0%
0 56434
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1427854
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1371420
96.0%
0 56434
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1427854
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1371420
96.0%
0 56434
 
4.0%

Interactions

2023-02-13T00:37:10.546251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:27.442836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:31.073955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:34.754624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:38.223244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:41.778007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:45.501188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:49.006034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:52.515362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:56.202132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:59.832855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:03.405499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:06.944298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:10.994255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:27.730353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:31.333303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:35.013517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:38.481959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:42.046604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:45.764655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:49.265175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:52.782009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:56.475727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:00.115186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:03.671030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:07.207009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:11.282012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:28.006960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:31.593884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:35.265909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:38.749817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:42.308360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:46.018469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:49.528122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:53.039226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:56.748415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:00.378603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:03.929050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:07.471001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:11.536888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:28.276640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:31.862984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:35.528913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:39.019072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:42.592365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:46.274660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:49.811530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:53.338494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:57.019301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:00.658866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:04.206832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:07.751022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:11.801830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:28.565603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:32.146528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:35.802490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:39.290861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:42.864650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:46.536723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:50.087933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:53.608568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:57.294181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:00.936006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:04.482267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:08.039246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:12.076353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:28.848123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:32.431989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:36.088099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:39.570490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:43.138878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:46.820220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:50.371504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:53.889879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:57.583523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:01.210355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:04.763033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:08.318197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:12.336914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:29.129897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:32.709303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:36.351720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:39.840066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:43.417172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:47.096013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:50.627096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:54.159949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:57.870301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:01.479299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:05.032129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:08.589979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:12.600204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:29.408398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:32.980200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:36.612524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:40.107285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:43.700324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:47.353559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:50.897253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:54.425947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:58.142939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:01.760325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:05.330215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:08.878905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:12.879203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:29.686027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:33.255315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:36.895300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:40.376967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:44.098945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:47.664422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:51.169940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:54.694938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:58.430086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:02.046061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:05.594635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:09.166070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:13.153483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:29.973177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:33.534948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:37.165102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:40.656731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:44.387062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:47.941105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:51.436902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:54.965509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:58.705109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:02.322156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:05.878640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:09.454335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:13.420628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:30.248243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:33.813543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:37.426534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:40.949178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:44.661958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:48.200041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:51.707194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:55.233552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:58.977357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:02.601263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:06.146294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:09.734060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:13.678881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:30.526495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:34.087019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:37.691991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:41.231221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:44.944885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:48.467177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:51.984199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:55.499006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:59.243716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:02.879711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:06.404842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:10.010235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:13.947082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:30.806046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:34.489016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:37.969104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:41.513280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:45.221911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:48.744917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:52.262987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:55.774257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:36:59.527968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:03.154357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:06.669816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T00:37:10.282964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-13T00:37:24.862865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
PipeIdInspectionsNo_IncidentsRisk_S*I/Inspectionsleakage_estimate_factorInspectionYearMonthsLastRevRisk_S*IYearBuiltDiameterLengthPressureNumConnectionsInspectionDaySeverityIncidenceProvinceMaterialNumConnectionsUndergas_natural
PipeId1.0000.385-0.029-0.029-0.0290.0040.129-0.029-0.0200.171-0.027-0.001-0.0680.0280.0190.0290.2970.1290.0100.348
Inspections0.3851.000-0.003-0.004-0.0030.356-0.073-0.003-0.4810.1850.119-0.1640.0700.0190.0280.0480.1400.1070.0100.449
No_Incidents-0.029-0.0031.0001.0000.9980.004-0.0451.000-0.030-0.0110.064-0.0280.0950.0030.2970.5130.0390.0380.0000.057
Risk_S*I/Inspections-0.029-0.0041.0001.0000.9980.004-0.0451.000-0.030-0.0120.064-0.0280.0950.0040.5200.6770.0340.0720.0000.129
leakage_estimate_factor-0.029-0.0030.9980.9981.0000.005-0.0440.998-0.030-0.0110.064-0.0280.0960.0020.2760.4510.0090.0430.0000.101
InspectionYear0.0040.3560.0040.0040.0051.0000.0060.004-0.0290.0160.131-0.0600.0790.0200.0340.0550.0720.0490.0000.071
MonthsLastRev0.129-0.073-0.045-0.045-0.0440.0061.000-0.045-0.0530.060-0.0470.014-0.0590.0120.0240.0350.0820.1190.0040.057
Risk_S*I-0.029-0.0031.0001.0000.9980.004-0.0451.000-0.030-0.0110.064-0.0280.0950.0030.3710.6330.0300.0570.0000.064
YearBuilt-0.020-0.481-0.030-0.030-0.030-0.029-0.053-0.0301.000-0.2020.0010.255-0.0370.0230.0490.0800.1610.1590.0060.224
Diameter0.1710.185-0.011-0.012-0.0110.0160.060-0.011-0.2021.0000.025-0.298-0.1880.0170.0170.0270.1600.1500.0030.307
Length-0.0270.1190.0640.0640.0640.131-0.0470.0640.0010.0251.0000.0620.5210.0020.0000.0000.0080.0060.0170.000
Pressure-0.001-0.164-0.028-0.028-0.028-0.0600.014-0.0280.255-0.2980.0621.000-0.1320.0250.0050.0090.1250.3110.0010.057
NumConnections-0.0680.0700.0950.0950.0960.079-0.0590.095-0.037-0.1880.521-0.1321.0000.0020.0300.0510.0150.0140.0330.054
InspectionDay0.0280.0190.0030.0040.0020.0200.0120.0030.0230.0170.0020.0250.0021.0000.0020.0030.1040.0180.0030.015
Severity0.0190.0280.2970.5200.2760.0340.0240.3710.0490.0170.0000.0050.0300.0021.0001.0000.0280.0740.0000.062
Incidence0.0290.0480.5130.6770.4510.0550.0350.6330.0800.0270.0000.0090.0510.0031.0001.0000.0410.1110.0000.055
Province0.2970.1400.0390.0340.0090.0720.0820.0300.1610.1600.0080.1250.0150.1040.0280.0411.0000.0940.0110.234
Material0.1290.1070.0380.0720.0430.0490.1190.0570.1590.1500.0060.3110.0140.0180.0740.1110.0941.0000.0110.323
NumConnectionsUnder0.0100.0100.0000.0000.0000.0000.0040.0000.0060.0030.0170.0010.0330.0030.0000.0000.0110.0111.0000.001
gas_natural0.3480.4490.0570.1290.1010.0710.0570.0640.2240.3070.0000.0570.0540.0150.0620.0550.2340.3230.0011.000

Missing values

2023-02-13T00:37:14.629891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-13T00:37:16.536881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PipeIdInspectionsNo_IncidentsRisk_S*I/Inspectionsleakage_estimate_factorInspectionDayInspectionYearInspectionDateMonthsLastRevRisk_S*ISeverityIncidenceProvinceTownYearBuiltMaterialDiameterLengthPressureNumConnectionsNumConnectionsUnderBoolBridlegas_natural
634534356922465100.00.0Thursday20202020-12-31240.040ValenciaBetera1993PE63.0000001.7780004.0000001
534760188341482600.00.0Thursday20212020-12-31230.040BarcelonaSabadell1995PE200.00000034.9599990.0250001
4790727189485681600.00.0Thursday20202020-12-31230.040ValenciaBetera1950AO50.79999916.4230004.0000001
4790765189485654600.00.0Thursday20202020-12-31230.040ValenciaBetera1950AO50.79999911.4430004.0000001
535324274990283600.00.0Thursday20212020-12-31230.040BarcelonaSabadell2005PE160.00000010.3770000.0250001
535302274925411600.00.0Thursday20212020-12-31230.040BarcelonaSabadell2005PE200.00000013.4970000.0251001
4794338189538742600.00.0Thursday20202020-12-31230.040ValenciaBetera1950AO50.79999952.9570014.0000001
534940274990929600.00.0Thursday20212020-12-31230.040BarcelonaSabadell1995PE200.0000003.4700000.0250001
534928188341464600.00.0Thursday20212020-12-31230.040BarcelonaSabadell1995PE200.0000001.3730000.0250001
534922189215318600.00.0Thursday20212020-12-31230.040BarcelonaSabadell2000PE250.0000008.9300000.0250001
PipeIdInspectionsNo_IncidentsRisk_S*I/Inspectionsleakage_estimate_factorInspectionDayInspectionYearInspectionDateMonthsLastRevRisk_S*ISeverityIncidenceProvinceTownYearBuiltMaterialDiameterLengthPressureNumConnectionsNumConnectionsUnderBoolBridlegas_natural
81993333980653100.00.0Friday20102010-10-08240.040TarragonaCalafell2008PE160.00.6000.150001
81992333980637100.00.0Friday20102010-10-08240.040TarragonaCalafell2008PE160.00.6010.150001
81991190852729100.00.0Friday20102010-10-08240.040TarragonaCalafell2004PE90.00.5010.150001
54716331062012100.00.0Wednesday20102010-10-06240.040TarragonaCalafell2008PE90.01.0000.150001
54709333980664100.00.0Wednesday20102010-10-06240.040TarragonaCalafell2008PE160.00.6010.150001
54218189142507100.00.0Wednesday20102010-10-06240.040TarragonaAmposta2000PE110.00.6940.150001
45975189141476100.00.0Tuesday20102010-10-05240.040TarragonaCalafell2000PE110.01.1880.150001
39756324551020100.00.0Tuesday20102010-10-05240.040BarcelonaSentmenat2008PE110.00.8020.100001
39473190908195100.00.0Tuesday20102010-10-05240.040AlicanteAlicante/Alacant2004PE200.00.9990.150001
2816340613298100.00.0Friday20102010-10-01210.040TarragonaCalafell2009PE90.01.1010.150001